Classification and characterization of text is of ever growing importance in defense and national security. The text classification task is an instance of classification using sparse features residing in a high dimensional feature space. Two standard (of a wide selection of available) algorithms for this task are the naive Bayes classifier and the Rocchio linear classifier. Naive Bayes classifiers are widely applied; the Rocchio algorithm is primarily used in document classification and information retrieval. Both these classifiers are popular because of their simplicity and ease of application, computational speed and reasonable performance. One aspect of the Rocchio approach, inherited from its information retrieval origin, is that it explicitly uses both positive and negative models. Parameters have been introduced which make it adaptive to the particulars of the corpora of interest and thereby improve its performance. The ideas inherent in these classifiers and in second generation wavelets can be recombined into new algorithms for classification. An example is a classifier using second generation wavelet-like functions for class probes that mimic the Rocchio positive template - negative template approach.
KEYWORDS: Signal to noise ratio, Fourier transforms, Modulation, Annealing, Active sonar, Backscatter, Numerical simulations, System identification, Detection and tracking algorithms, Computer simulations
In active sonar the acoustic backscatter may contain multiple specular components, resonant components, Gaussian, reverberation and other noises as well as multipath effects. One purpose of sonar signal processing is to extract information from the backscatter for underwater target classification. Matching pursuit together with a variation of annealing can be used to separate the specular components and to estimate the corresponding delays and intensities. The effectiveness of this algorithm for linear frequency modulated transmit signals is demonstrated by numerical simulation.
A new feature for the classification of echoes of a transmitted signal based on the generalized target description is described, and a framework for adaptive classification using it is presented. The generalized target description is a parametric model for the target impulse response. The feature is an order parameter from this model, which can be computed empirically from the growth rate of power as a function of a scale for a certain wavelet transform of the echo. A set of acoustic backscatter data consisting of returns from a mine and a rock with a linear FM transmit signal was analyzed. Parameters for the wavelet were computed from training sets so that this feature correctly distinguished 94% of the returns is test sets at 15 dB. The effectiveness of this feature as a classifier was found to degrade reasonably under increasing levels of synthetically generated reverberation noise. The simplest generalized target description model, a single order single center scatterer, was used. This model is not a realistic representation of the target impulse responses of either of the two objects, nevertheless it captured enough of the difference between the two to provide an effective classification tool.
KEYWORDS: Wavelets, Interference (communication), Signal detection, Wavelet transforms, Signal to noise ratio, Error analysis, Warfare, Transform theory, Data centers, Signal processing
Short, high frequency bursts are found in flow noise data from a transient regime in a pipe flow experiment. These bursts are directly detectable in accelerometer measurements, but processing is necessary to detect them in synchronous hydroacoustic measurements. These signals can be detected and recovered using wavelet thresholding. New variations on methods of choosing the threshold and exploiting translations of the wavelet basis are described, then applied to the estimation of these bursts.
We evaluate via numerical simulation the performance of a detection algorithm applicable to unknown transients in acoustic signals. This is a filter-then-detect scheme in which the filtering is accomplished by thresholding in the wavelet domain. The incoming time series is separated into 'signal' and 'noise' in the wavelet transform domain. The set of coefficients representing the 'signal' is inverse-transformed back to the time domain. An energy threshold is applied to the recovered signal time series. The performance of the wavelet filtering as a part of the detection process is determined by constructing the receiver-operating-characteristic curve, which displays the dependence of the probability of detection on the probability of false alarm as functions of an energy threshold.
The wavelet transform is a bank of convolution filters indexed by scale; each scale of the transform of a signal is a filtered version of that signal. Here we explore the use of the Morlet wavelet to filter one coordinate of a dynamical system in order to visualize certain aspects of the geometry of the dynamics. This technique is a natural generalization of the differential phase plane representation.
The quantification of the textural differences between regions and the classification of subregions by their texture is an important component of image analysis. Here we demonstrate that texture quantifiers based on the wavelet transform of an image are good discriminators of texture differences.
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